- remove disconnect_task
- move disconnect logic to a per-request handler that wraps cleanup operation and directly polls the request state with throttling
- exclusively signal disconnect with CancelledError
- rework completions endpoint to follow same approach as chat completions, share some code
- refactor OAI endpoints a bit
- correct behavior for batched completion requests
- make sure logprobs work for completion and streaming completion requests
- more tests
- remove ToolConfig, reduce to a single `tool_format` argument and hard-code extra args like start/end tokens
- dispatch to short, self-contained (and probably easily vibe coded) parser for each model type
- remove autodetection (seems infeasible since parsing effectively starts during streaming, and there is overlap between tool formats for different models)
- streamline xml parser and dedicate to qwen3_coder models
- add parsers for glm4.x, minimax-m2.x and mistral (seems shaky, probably because mistralai don't validate against hf)
- update docs
- move tool config from template_vars to separate yml config
- new per-gen stream collector used for both streaming and non-streaming requests to ensure logic is consistent for both
- move responsibility for switching between phases to stream collector
- collect tool calls during streaming and parse at the end of each gen
- prevent streaming empty content spans (be nice to clients)
- correctly aggregate usage stats for n>1 requests, always emit with last chunk in last gen to finish
- collect logprobs in model wrapper and correctly handle logprobs for multi-token chars etc.
- respect top_logprobs argument in request
- handle a number of edge cases like <think> tag being part of held string, etc.
- retain tool parsing and inference-abort fixes from #413, apply similar fix to non-stream request as well
Still TODO:
- testing and validation with more models and tool schemas (tested on Qwen so far)
- enable JSON constraint for JSON tool models
- possibly some pydantification
- documentation
Also remove the intermediate base_seq_len and target_seq_len variables
to make code clearer.
If paged mode is off, max_seq_len becomes the prime mover since batching
is unavailable.
Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
The previous changes broke existing configs and max_seq_len was
force-overriden to 4096. This helps single-user setups since they
do not really benefit from the split cache_size max_seq_len mechanism
(except if batching).
cache_size is still the prime mover in exl3 due to its paging mechanism.
Ideally, for multi-user setups, cache_size should take as much VRAM
as possible and max_seq_len should be limited.
Breakdown:
cache_size and max_seq_len specified -> values
only cache_size/max_seq_len specified -> max_seq_len = cache_size and vice versa
neither specified -> cache_size = 4096, max_seq_len = min(max_position_embeddings, cache_size)
Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
- Cache size is now given only by the cache_size config option. Default is 4096 (user should always override to max out VRAM)
- max_seq_len, if not overridden in the config, will default to the model's config.json
- max_seq_len is reduced to be no larger than the cache
